by Jack Clark

Deep learning’s dirty data secret: modern deep learning approaches require a ridiculous quantity of data. How much data? A hacker house in SF used3.8 terabytes of bandwidth in July. No coincidence that one of its residents is George Hotz, founder of self-driving car startupComma.ai. The house has now upgraded to an ‘unlimited’ bandwidth plan, making Hotzfoot the bill for the bit-flurry.

Show me the research: Spotting the difference between an AI startup with genuine technology and one that has re-implemented commodity systems is tricky. It’s easier to judge if the company participates in the wider AI research community. Therefore points should be awarded to robotics companyBrain Corporation, text analysis startupMaluuba, and image recognition outfitCurious AI, which have all published papers recently. Keep them coming! (Extra kudos to Curious AI forpublishing code.)

Spy VS Spy! Seven autonomous computer systems battled each other at the Darpa Cybersecurity Challenge in Las Vegas last week. ‘Mayhem’, thewinner, was built by CMU startupFor All Secure. The system performed automated program analysis (via symbolic execution) to find and exploit weaknesses in running programs. It appears to use asmart scheduler to let it run multiple checks on a piece of software in parallel, then selectively pause certain jobs to throw resources at promising vulnerabilities. This lets it efficiently explore the vast underbelly of the program and selectively focus on weak points, like a sophisticated, thieving octopus. (Unfortunately,someof the papers are paywalled.) Podcast with more informationhere.

Care for a little AI with your global government, sir? Google DeepMind would eventually like to donate its technologies to the United Nations,according to CEO Demis Hassabis.

Deep learning chips: Typical x86 processors are a bad fit for (most) modern AI tasks. GPUs are a bit better, but still not optimal. So expect new hardware substrates for AI. Microsoft will launch a new cloud service within a few months that lets people accelerate their neural network workloads with FPGA co-processors,said Qi Lu at theScaled Machine Learning Conference. Google has already indicated it will offer its TPUs to people to accelerate TensorFlow workloads. Startup Nervana plans to produce its own chip to accelerate its ‘Neon’ software further. There are also numerous stealth chip and hardware startups that are re-thinking systems around deep learning (think: tens of thousands to millions of processors for low-precision matrix multiplication, etc).

GoodAI, a European AI research group founded by game developer Marek Rosa, has fleshed out its research and development roadmap and begun work on analyzing the overall AI landscape. It’s also developed some software for prototyping AI systems “with highly dynamic neural network topologies” named Arnold. It’s verypretty! The company will release it as open source software in a few months months, Rosa tells me.

Open VS Closed AI development? “Developing a joint set of openness guidelines on the short and long term would be a worthwhile endeavor for the leading AI companies today,”says Victoria Krakovna, co-founder of the Future of Life Institute.

It’s easy to put 1M+ cores(tm) on a die, but it’s hard to design a processor and a programming model that lets one utilize them efficiently across a wide variety of parallel workloads. That’s what CUDA represents.

But until Deep Learning, IMO CUDA was just a better mousetrap in search of a mouse. While it scored multiple stunning and surprising HPC victories in the past decade, its marketing was focused on a brutal head to head with Intel to win the LINPACK benchmark and thus win supercomputer contracts.

But as Tim Dettmers has described eloquently in a 2015 blog entry (http://timdettmers.com/2015/07/27/brain-vs-deep-learning-singularity/), LINPACK was and remains the wrong benchmark. NVIDIA’s GPUs were capable of much more, and now they have a decade of legacy CUDA code they can exploit to design future processors and maybe even ASICs. No one else has this advantage, and further, only Intel and AMD can fund processor development by selling to gamers and consumers. I call that advantage the Gaming/Industrial Complex.

As such, even if NVIDIA were to develop a CUDA ASIC (and arguably GP100 with its exclusive FP16 and FP64 support is a small step in that direction), these advantages will stay with them. And their competition in the consumer space keeps dropping the ball lately.

If I were an NVIDIA competitor, I’d stop trying to lure people away from CUDA and its decade of useful code, and I’d start trying to run CUDA better than NVIDIA GPUs. That’s a tall order, but until Moore’s Law runs out, I don’t see any other way to kick sand in the face of said Gaming/Industrial Complex.

[…] on specially designed chips. The company first announced the Catapult system years ago and said in August of this year that it would make it available as a service other developers could rent. Meanwhile, a new company […]